Integrating an LLM with SAP works by wrapping SAP's existing interfaces, mainly OData services exposed through SAP Gateway, BAPI and RFC calls, or the SAP Business Technology Platform, inside a custom MCP or API layer that the model can call as discrete tools such as check inventory, retrieve purchase order status or look up a customer record. SAP's own Joule assistant covers a subset of use cases within SAP's ecosystem, but a custom integration lets a company connect any LLM, including an on-premise model, to the same underlying SAP data without being confined to Joule's roadmap. Authentication should map to the acting user's own SAP roles rather than a generic technical user, so the model never has broader access than the employee asking the question, and every write operation such as creating a sales order needs its own approval step rather than direct execution. Read-only reporting and lookup tools are the safest starting point before extending into transactional actions. Nanobase AI, an NVIDIA Inception Program member, builds SAP integration layers that expose these operations safely to both commercial and self-hosted language models.
Which SAP interface fits which use case
SAP exposes several distinct integration surfaces, and picking the wrong one for a given tool is a common source of unnecessary complexity.
| SAP interface | Nature | Good fit for |
|---|---|---|
| OData services via SAP Gateway | REST-like, modern, easiest to wrap | Read-only lookups: order status, inventory, customer master data |
| BAPI / RFC | Older, function-module based, needs a connector library or middleware | Transactional operations tightly coupled to existing business logic |
| SAP Business Technology Platform (BTP) | Integration and extension platform sitting alongside core SAP | Building a dedicated integration layer without touching core ABAP |
| SAP Datasphere / analytics layer | Data and reporting focused | Aggregate reporting questions, not single-record lookups |
Most enterprise LLM integrations should start with OData through SAP Gateway because it maps cleanly onto discrete tools with JSON responses, while BAPI/RFC calls are better reserved for operations that must reuse existing SAP business logic, such as a purchase order creation flow that already runs extensive validation.
A middleware layer beats a direct connection
Connecting an MCP server or custom integration straight to an SAP system without an intermediary tends to duplicate logging, rate limiting and error handling that a company's existing API gateway or SAP Process Integration layer likely already provides for other consumers. Routing the LLM integration through that same middleware, rather than around it, means the assistant inherits existing monitoring and throttling instead of becoming an unmonitored second path into SAP. This also isolates the AI layer from internal SAP version changes, since the middleware's interface can stay stable even if the underlying SAP release changes.
A phased rollout across SAP modules
- Phase 1, read-only lookups: expose a handful of narrow tools such as checking inventory in MM, order status in SD, or an invoice status in FI, each returning only the fields needed for that question.
- Phase 2, drafting and summarization: let the assistant draft documents, such as a proposed purchase requisition or a summarized aging report, without submitting anything to SAP directly.
- Phase 3, gated writes: enable actual creation or update operations, routed through the same SAP validation and approval workflows a human user would go through, with a confirmation step before submission.
- Phase 4, cross-module reasoning: once individual module tools are stable, let the assistant combine them, for example checking stock in MM before drafting a sales order in SD.
Skipping straight to phase 3 or 4 is the most common cause of failed SAP AI projects, since it exposes write risk before the team has validated that the model reliably picks the right tool and fields on read-only operations first.
Authorization follows SAP's own role model
The safest authorization pattern maps the acting user's own SAP authorization roles onto what the assistant can do on their behalf, rather than granting the integration a single broad technical user. This means a warehouse clerk's AI assistant session can check stock but not view financial postings, exactly as if they were using SAP GUI directly, because the underlying authorization check is the same one SAP already enforces.
Frequently asked questions
Is SAP Joule a substitute for a custom integration?
Joule covers a growing set of use cases natively within SAP's own interface and ecosystem. A custom integration remains worthwhile when a company needs a specific model, wants cross-system reasoning that spans SAP and non-SAP systems, or needs the assistant reachable from tools outside SAP's own UI, such as Slack or an IDE.
How long does a first SAP OData integration typically take?
This depends heavily on how many SAP modules and tools are in scope and the state of existing OData services, so it is best estimated against the specific systems involved rather than generalized; a narrow single-module read-only pilot is a materially different project than a multi-module transactional rollout.
Can the assistant work with an older SAP ECC system instead of S/4HANA?
Yes, both expose OData and BAPI/RFC interfaces, though the specific services available and the amount of custom ABAP wrapping needed can differ meaningfully between ECC and S/4HANA, so an assessment of the existing service layer is a necessary first step either way.
How Nanobase AI helps
Nanobase AI, an NVIDIA Inception Program member, builds SAP integration layers following this phased, middleware-first approach, mapping OData, BAPI and BTP interfaces to scoped tools without bypassing SAP's own authorization and validation logic. Related guidance on gating write actions is in llm-write-actions-sap-purchase-orders, and data exposure limits are covered in integrate-llm-erp-limit-data-exposure.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.